This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Yunes, T. H. Articles by Tayur, S. Search for Related Content

Building Efficient Product Portfolios at John Deere and Company

Department of Management Science, School of Business Administration, University of Miami, Coral Gables, Florida 33124-8237
Deere & Company, Technology Center, Moline, Illinois 61265-8098
Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890
Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890
tallys{at}miami.edu
napolitanodominic{at}johndeere.com
awolf{at}andrew.cmu.edu
stayur{at}andrew.cmu.edu

John Deere & Company (Deere), one of the world’s leading producers of machinery, manufactures products composed of various features, within which a customer may select one of a number of possible options. On any given Deere product line, there may be tens of thousands of combinations of options (configurations) that are feasible. Maintaining such a large number of configurations inflates overhead costs; consequently, Deere wishes to reduce the number of configurations from their product lines without upsetting customers or sacrificing profits. In this paper, we provide a detailed explanation of the marketing and operational methodology used, and tools built, to evaluate the potential for streamlining two product lines at Deere. We illustrate our work with computational results from Deere, highlighting important customer behavior characteristics that impact product line diversity. For the two very different studied product lines, a potential increase in profit from 8% to 18% has been identified, possible through reducing the number of configurations by 20% to 50% from present levels, while maintaining the current high customer service levels. Based on our analysis and the insights it generated, Deere recently implemented a new product line strategy. We briefly detail this strategy, which has thus far increased profits by tens of millions of dollars.

Subject classifications: industries; machinery; marketing; retail/product line optimization; production; applications.
History: Received April 2004; revision received June 2006; accepted June 2006.

This article has been cited by other articles:

				C. W. Chan and V. F. Farias Stochastic Depletion Problems: Effective Myopic Policies for a Class of Dynamic Optimization Problems Mathematics of Operations Research, May 1, 2009; 34(2): 333 - 350. [Abstract] [PDF]

				A. Alptekinoglu and C. J. Corbett Mass Customization vs. Mass Production: Variety and Price Competition MSOM, January 1, 2008; 10(2): 204 - 217. [Abstract] [PDF]